Prevalence, risk factors, and antimicrobial resistance of endemic healthcare-associated infections in Africa: a systematic review and meta-analysis

Background Healthcare-associated infections (HCAI) place a significant burden on healthcare systems globally. This systematic review and meta-analysis aimed to investigate the prevalence, risk factors, and aetiologic agents of endemic HCAI in Africa. Methods MEDLINE/PubMed, CINAHL, and Global Health databases (EBSCOhost interface) were searched for studies published in English and French describing HCAI in Africa from 2010 to 2022. We extracted data on prevalence of HCAI, risk factors, aetiologic agents, and associated antimicrobial resistance patterns. We used random-effects models to estimate parameter values with 95% confidence intervals for risk factors associated with HCAI. This study was registered in PROSPERO (CRD42022374559) and followed PRISMA 2020 guidelines. Results Of 2541 records screened, 92 were included, comprising data from 81,968 patients. Prevalence of HCAI varied between 1.6 and 90.2% with a median of 15% across studies. Heterogeneity (I2) varied from 93 to 99%. Contaminated wound (OR: 1.75, 95% CI: 1.31–2.19), long hospital stay (OR: 1.39, 95% CI: 0.92–1.80), urinary catheter (OR: 1.57, 95% CI: 0.35–2.78), intubation and ventilation (OR: 1.53, 95% CI: 0.85–2.22), vascular catheters (OR: 1.49, 95% CI: 0.52–2.45) were among risk factors associated with HCAI. Bacteria reported from included studies comprised 6463 isolates, with E. coli (18.3%, n = 1182), S. aureus (17.3%, n = 1118), Klebsiella spp. (17.2%, n = 1115), Pseudomonas spp. (10.3%, n = 671), and Acinetobacter spp. (6.8%, n = 438) being most common. Resistance to multiple antibiotics was common; 70.3% (IQR: 50–100) of Enterobacterales were 3rd -generation cephalosporin resistant, 70.5% (IQR: 58.8–80.3) of S. aureus were methicillin resistant and 55% (IQR: 27.3–81.3) Pseudomonas spp. were resistant to all agents tested. Conclusions HCAI is a greater problem in Africa than other regions, however, there remains a paucity of data to guide local action. There is a clear need to develop and validate sustainable HCAI definitions in Africa to support the implementation of routine HCAI surveillance and inform implementation of context appropriate infection prevention and control strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-024-09038-0.


Background
Healthcare-associated infection (HCAI) is a global health challenge that seriously threatens patient safety by significantly increasing the morbidity and mortality associated with healthcare exposure, hospital length of stay, longterm disability, financial burden, and contributing to the spread of multidrug-resistant (MDR) pathogens [1][2][3][4][5][6].HCAI are thought to have a higher burden in African countries than in high-income countries, yet are understudied and underreported [7].Data summarised in previous reviews report endemic HCAI prevalence of up to 15.5% in general wards and can reach 50% in intensive care units (ICU) in Africa [7][8][9].However, it is important to emphasise that most of these studies focus on a particular institution, often tertiary and/or teaching facilities and may not reflect the situation at a national level.
Maintaining HCAI surveillance is a challenge in wellresourced healthcare settings [10] and even more so in low-resource ones, however defining the magnitude of HCAI is key to placing it in context for policy makers.It is anticipated that HCAI is responsible for a significant burden of disease in contexts where there is a lack of basic infection prevention and control (IPC) capacity.A further complication associated with HCAI is antimicrobial resistance (AMR), which has emerged as a major public health problem worldwide, with bacteria associated with HCAI disproportionately resistant to antibiotics [11,12].In low-resource settings, HCAI is likely to be exacerbated by infrastructural problems i.e. lack of water in hospitals (particularly safe water), poor hygiene and sanitation, understaffing, failure to implement or lack of antimicrobial policies, shortage of basic laboratory equipment for diagnosis, suboptimal adherence to safe practices by health care workers, limited compulsion to report HCAI, and limited funding [13,14].
The World Health Organization (WHO) has developed several IPC guidelines and documents founded on the core components framework, a key aspect of which is HCAI surveillance [15,16].Data from HCAI surveillance can be used to quantify the HCAI burden, evaluate HCAI trends over time, pinpoint areas where HCAI prevention efforts need to be targeted and improved, and IPC strategies to reduce HCAI [15,17].
Despite the significant impact of HCAI in Africa, there is a lack of up-to-date and comprehensive information on the prevalence, risk factors, and AMR of endemic HCAI in the region.This systematic review and meta-analysis of endemic HCAI in Africa is an update of the last 13 years since the last review published in 2011 by Nejad and colleagues [8], containing data published from 1995 to 2009.Here, we aimed to provide an up-to-date and comprehensive overview of the prevalence, risk factors, aetiology, and AMR of endemic HCAI in Africa.

Search strategy and eligibility criteria
For this systematic review and meta-analysis, we searched the MEDLINE/PubMed, CINAHL, and Global Health (EBSCOhost interface) electronic databases.To ensure literature saturation, the reference lists of the included studies were scanned to identify and capture other relevant studies, and Google Scholar was used to identify and screen studies citing them.Literature published in French and English between January 2010 and December 2022 was considered.The search was limited to human subjects and the most common HCAI encountered in African countries, including surgical site infections (SSI), healthcare-associated urinary tract infections (HA-UTI), healthcare-associated bloodstream infections (BSI), and hospital-acquired pneumonia/ventilator-associated pneumonia.The search strategy was developed based on the outcomes of interest (prevalence, risk factors, and antimicrobial resistance profile of bacteria isolated from HCAI).The complete search strategy with keywords and MeSH terms using the Boolean terms "OR" and "AND" is provided in supplemental materials (Supplement pp3-4).
We included observational studies (case-control, longitudinal, cohort, and cross-sectional) that prospectively or retrospectively explored the outcomes of interest (prevalence, risk factors, aetiologic agent and the antimicrobial resistance profile of bacteria isolated from HCAI) in all age groups in inpatient settings.We excluded the following types of studies due to the lack of relevance to our research question, or due to their limitations in allowing us to identify risk factors for HCAI or assess AMR: those reporting only the prevalence of HCAI without including at least one of the other outcomes of interest, those reporting on specific microorganisms causing HCAI, those reporting risk factors associated with HCAI but not reporting the effect size measures of these factors, or those reporting on HCAI outbreaks.Case series, case reports, editorials, commentaries, conference proceedings, preprints, reviews, previous systematic reviews and meta-analyses, and unpublished articles were excluded.Research letters to the editor containing data that met these criteria were included.We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis 2020 (PRISMA) guidelines [18] (Supplement pp5-6) while conducting this systematic review and meta-analysis.The protocol was registered and published in PROS-PERO (CRD42022374559).

Data extraction
Two reviewers (GKB and EM) independently screened the titles and abstracts of studies according to the inclusion criteria.Any disagreements were resolved by discussion and consensus.In cases of further disagreement, a third reviewer (NF) holding a casting vote was consulted.The full-text review then occurred, and each reviewer independently screened the full texts against the inclusion and exclusion criteria, with disagreements resolved by consensus or discussion with a third reviewer (NF), if necessary.
Two reviewers (GKB and EM) extracted the data in a pre-piloted Excel spreadsheet.This included the study's first author, publication year, region, country, population, study design, study population, surveillance definition used to define HCAI, type of HCAI and its prevalence, risk factors associated with HCAI (if analysed) and their effect size, and isolated bacteria and their antimicrobial resistance profile (if reported).The extracted data were compared, and discrepancies were resolved through discussion.
To assess the risk of bias in the included studies, we used a modified Critical Appraisal Skills Programme (CASP) checklist, designed to fit our research question and the Newcastle-Ottawa scale (NOS) for assessing the quality of non-randomized studies in meta-analyses (Supplement pp7-8) [19].Both quality scales and domainbased tools were used simultaneously to assess all the included studies.The CASP checklist used in this systematic review assessed four domains: (i) appropriateness of the study population or participant recruitment, (ii) eligibility criteria, (iii) valid methods to identify the HCAI, and (iv) selective non-reporting or under-reporting of outcome measures.The NOS quality instrument score was awarded a star (corresponding to the points) for each area.It assesses the study's area of selection (maximum of 5 points), comparability (maximum of 2 points), and outcomes (maximum of 3 points).After summing the star points, the studies were classified into three categories: good (7-10 points), moderate (5-6 points), and poor (0-4 points).The risk of bias assessment was performed by GKB and EM, and any disagreements were resolved by consensus.

Data synthesis and statistical analysis
The extracted data were reported as study-level summary estimates, and qualitative and quantitative techniques were used to synthesise them.The primary outcome was the prevalence of HCAI stratified by HCAI type.Secondary outcomes included risk factors associated with HCAI and AMR profile of reported bacteria causing HCAI.All estimates were expressed as proportions with restricted maximum likelihood (REML) 95% confidence intervals (CI) and presented in forest plots using a random-effects meta-analysis.Heterogeneity was assessed using Higgins I 2 statistic, Cochran's Q test, and tau-squared τ 2 .The 95% CI around τ 2 and I 2 were calculated to assess confidence in these metrics.We set a stringent I 2 threshold of > 75% as indicative of significant heterogeneity, but we also assessed this heterogeneity through the CIs and localisation on the forest plot [20].Significant heterogeneity in prevalence between HCAI types was expected since some types of HCAI are more commonly reported, and this was addressed by stratification by HCAI type [8].In the case of high heterogeneity, we determined it was not appropriate to pool estimates of HCAI prevalence [21].
To provide pooled risk estimates for the factors associated with HCAI, an exploratory meta-regression analysis was performed for risk factors found to be significant in at least four of the included studies.This threshold was chosen to ensure that the meta-regression analysis was based on an adequate number of studies to provide a robust and meaningful relationship between the pooled risk estimates and the outcome of interest.This approach helps reduce the risk of random chance or spurious associations and increases the validity of the metaregression results.We presented the pooled estimated effect size (odds ratio (OR)) and degrees of heterogeneity with their 95% CI and p-values.The OR were computed and reported on a log scale.A statistically significant (p < 0.05) coefficient indicated an association between the effect estimate for HCAI and the associated risk factors.
We used quantile regression to calculate the AMR median rates and interquartile ranges (IQR) of the reported bacteria.We performed post hoc sensitivity analyses stratified by study quality to further estimate the robustness of the relevance estimates.All analyses were performed using the meta (version 6.1-0) and metafor (version 3.8-1) packages in R.

Role of the funding sources
The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.
Most studies were conducted at university/teaching hospitals (n = 44, 47.8%) and other tertiary hospitals (n = 37, 40.2%).Approximately half of the included studies were conducted in the surgical (n = 31, 33.7%) and obstetrics wards (n = 18, 19.6%).Twenty (21.7%) studies were conducted in ICUs, while 16 (17.4%)reported HCAI from all wards of their hospitals.Most studies used the CDC HCAI surveillance definitions (n = 49, 53.3%), with a few examples of ECDC (n = 3, 3.3%) and WHO (n = 3, 3.3%) definitions being used.Two studies used both the CDC and ECDC definitions, while the IDSA and NINSS HCAI surveillance definitions were each used in one (1.1%)study.Among the studies that used CDC definitions, four adapted them to the local context.A total of 33 (35.9%) studies used their own surveillance definitions or did not report the definitions used in diagnosing HCAI.Study characteristics are summarised in Table 1.
The NOS and CASP risk bias assessment findings are detailed in the "Supplemental Materials" section (Supplement pp9-15).Of the 92 included studies, 13 (14.1%),30 (32.6%), and 49 (53.3%) were assessed to be of poor, moderate, and good quality, respectively.The validity of the methods used to identify the HCAI, the sample size, and selective non-reporting or under-reporting of outcome measures were sources of bias.
Twenty studies reported bacterial antimicrobial susceptibility profiles.Bacteria commonly exhibited resistance to multiple antibiotics.A concerning level of resistance to third generation cephalosporins (70.3%, IQR: 50-100) was observed among Enterobacterales; 70.5% (IQR: 58.8-80.3)S. aureus were Methicillin Resistant; and 55% (IQR: 27.3-81.3)Pseudomonas spp.were resistant to all agents tested (Table 5).We did not perform AMR analysis for HCAI types nor by UN African region because most studies reported the overall AMR and not per HCAI types and because there were only 20 studies.

Discussion
The prevalence of HCAI in Africa is clearly high, and bacteria associated with infections are frequently antimicrobial-resistant. Available data also suggest that risk factors for HCAI in Africa are entirely predictable and can be mitigated through implementation of IPC programs and many tools to address the challenge of HCAI exist.
Overall, the HCAI prevalence ranged between 1.6 and 90.2% (median 15%).These rates are higher than pooled prevalence reported in Europe (6.5%)[6], Southeast Asia (9%) [113], the United States (4%) [114], Australia (9.9%)   [115], and comparable to the pooled prevalence reported in a previous meta-analysis from developing countries (15.5%) [7].The high prevalence of HCAI in Africa could be due to inadequate infection control and prevention measures which are often hindered by limited capacity for infection prevention and control, poor laboratory support, and limited funding [13].Previous studies have demonstrated suboptimal adherence to hand hygiene protocols among healthcare workers in Africa that can be attributable to factors such as absence of safe water in healthcare facilities, inadequate healthcare built environment, inadequate knowledge and training, lack of personnel, and heavy workload [13,116,117].Prevalence of HCAI did not vary much when analysed by HCAI types, with pneumonia, BSI, and UTI having medians of 20-21% and SSI 12%.These infections are largely associated with medical devices and can be prevented through appropriate infection prevention and control measures.Hand and environmental hygiene as well as injection safety practices should be promoted in African healthcare facilities.Contrary to previous studies that reported SSI as the most common HCAI in healthcare facilities in Africa, our study showed that SSI has a lower median prevalence than other HCAI, although high heterogeneity was present [8,118].After sensitivity testing that excluded studies with poor and moderate quality, this ranking remained the same.There is a need for more routine data to mitigate the impact of bias.Nevertheless, this prevalence is high and lack of adequate infection control before, during and after a surgical procedure coupled with non-compliant surgical antimicrobial prophylaxis are areas that can be addressed to reduce SSI in Africa [116].
Despite the increase in HCAI surveillance studies from the previous systematic review, overall quantity and quality of HCAI data remain poor.CDC and ECDC HCAI surveillance definitions are currently the most widely used, however, applying these definitions in African settings is typically difficult because they require diagnostic facilities such as microbiological laboratories and complex imaging (CT scan, MRI), which are frequently not available [8,15,119,120].Consequently, HCAI surveillance remains a significant challenge and the burden of     HCAI is poorly described in Africa [3].Comparing HCAI rates within and between countries is critical for raising awareness about HCAI and its prevention and control; however, it necessitates standardised approaches, including uniform definitions.The World Health Organisation (WHO) and US Centers for Disease Control and Prevention are actively addressing this issue by endeavouring to develop and validate a set of definitions and diagnostic criteria for different HCAI syndromes that will be useful in the absence of a full range of diagnostic microbiology or radiology facilities.The WHO has the authority to advocate for the adoption of standard definitions and this will facilitate the integration of African data into broader international datasets.None of the risk factors for HCAI in Africa were a surprise and were consistent with global data [121].Patients with multiple comorbidities or complicated chronic illnesses are more susceptible to frequent hospitalisation, which increases their risk of HCAI and colonisation or infection with multidrug-resistant pathogens.To mitigate the burden of HCAI, several strategies can be implemented [121].There is already evidence globally, that interventions such as hand hygiene, environmental cleaning, surveillance, and multimodal approaches are costeffective for the prevention and control of HCAI [122].Overall, implementing a multimodal approach to HCAI prevention and control in Africa is necessary to reduce the burden of these infections and mitigate the risks to patient safety, and surveillance to quantify the problem and guide local action is key.Implementing a multimodal approach to IPC including enhancing healthcare worker training, implementing evidence-based IPC intervention bundles, and establishing effective surveillance systems sits at the heart of the WHO Core Components of IPC strategy and can be used to reduce the burden of these infections [15].It is anticipated this holistic strategy will help to address the complex challenges associated with HCAI in Africa, promoting patient safety and contributing to the overall improvement of healthcare systems on the continent.
The most frequently reported bacteria were often resistant to multiple antimicrobial agents that are commonly available in most countries in Africa.These bacteria are typical nosocomial pathogens and among the priority AMR bacterial pathogens identified by WHO.Resistance to fluoroquinolones and β-lactam antibiotics (i.e.cephalosporins, penicillins, and carbapenems), which are typically first-line empirical therapy for severe infections poses a significant threat to patient safety, as AMR further complicates HCAI treatment.
Limitations of this study include the limited number of studies, especially of studies that reported bacterial aetiology and associated AMR profile.Most papers reported aggregate data that were not broken down by clinical speciality or hospital department or age.Further there was an absence of studies from the majority of African countries.Many studies have not described the effect size measures (OR, p-value, and confidence interval) of risk factors.In addition, most studies were conducted in teaching hospitals, with minimal data from district hospitals.Quality of the included studies was highly heterogeneous, however, we addressed this by sensitivity tests and ranking of reported HCAI rates was not affected.Lack of consistency in methods for assessing AMR make these data challenging to compare across studies, nor was there enough AMR data to report by HCAI types.This study did not report on the mortality due to HCAI as majority of included papers did not report this measure.

Conclusions
Here, we provide a comprehensive review of the current HCAI situation in Africa.HCAI are a major problem in Africa, with a high prevalence, multiple risk factors, and increasing resistance to antimicrobial agents.It is imperative to build on this work by developing and validating HCAI definitions adapted to African settings and there is a pressing need to move HCAI surveillance beyond the realm of research studies and establish it as part of routine practice including in primary and secondary healthcare facilities.These measures are essential for policymakers to develop, evaluate, and improve appropriate HCAI prevention and control interventions to reduce the HCAI burden in Africa, AMR, and ultimately enhance the quality of healthcare in Africa.

Fig. 1
Fig. 1 Flowchart of articles screening and selection

Table 1
Characteristics of included studies

Table 1
(continued) HCAI Healthcare associated infection, PPS Point Prevalence Survey, IDSA Infectious Diseases Society of America, ECDC European Centre for Disease Prevention and Control, CDC Centers for Disease Control and Prevention, NHSN National Healthcare Safety Network, NNIS National Nosocomial Infections Surveillance System, ICU Intensive Care Unit, NR Not Reported, WHO World Health Organisation, NINSS National Integrated Non-Communicable Diseases Surveillance System a Classified among the CDC definitions

Table 2
Prevalence ranges of HCAI syndromes in random effects model SSI surgical site infection, BSI Bloodstream Infection, UTI Urinary Tract Infection, PN Pneumonia, CI confidence intervals a Studies that reported all the four HCAI without giving prevalence for each type b Excluding low quality studies c Including only good quality studies

Table 3
Exploratory Meta-regression analysis of risk factors associated with HCAI in Africa PVC peripheral venous catheter, CVC central venous catheter, Std error standard error, CI confidence intervals, OR odds ratio

Table 4
Bacteria reported in different HCAI clinical syndromes SSI surgical site infection, BSI Bloodstream Infection, UTI Urinary Tract Infection, PN Pneumonia Included all the four types of HCAI without separating them a Included catheter-associated UTI b Included catheter-related BSI, central line-associated BSI c Included healthcare-associated pneumonia, low respiratory tract infections, and ventilator-associated pneumonia d

Table 5
Median resistance rates with interquartile ranges of selected bacteria to selected tested antibiotics CoNS Coagulase negative S. aureus, Co-amoxiclav Amoxicillin-clavulanic acid